Inspect Volcano workloads faster with Headlamp

Headlamp and Volcano: A Unified Interface for Batch Workloads

Kubernetes was originally designed for long-running services, but batch, AI/ML, and HPC workloads often require different scheduling and resource management strategies. Volcano extends Kubernetes with features like queues, priorities, quotas, and gang scheduling, enabling more efficient handling of dynamic and resource-intensive jobs. The integration of the Volcano plugin into Headlamp brings these extended capabilities into a single, unified interface, making it easier to manage and troubleshoot complex workloads.

Key Resources in the Volcano Plugin

The Volcano plugin surfaces three core resource types directly in Headlamp: Jobs, Queues, and PodGroups. Each of these resources plays a critical role in managing batch workloads within Kubernetes. Jobs define batch workloads as a set of tasks and the Pods they create. Queues divide cluster capacity between teams or workloads using quotas and priorities. PodGroups tie a group of Pods together so the scheduler can treat them as a single unit for gang scheduling.

The plugin provides dedicated list and detail views for each of these resources under a Volcano section in the sidebar. This allows users to quickly navigate between Jobs, Queues, PodGroups, and related events without having to switch between multiple tools or interfaces.

Job View: Central to the Plugin Experience

The Job view is the center of the plugin experience. In the list view, users can quickly understand the basics of a workload, including its status, queue, running versus minimum-available values, task count, and age. The detail view provides more in-depth information, such as task details, Pod status, related Queue and PodGroup links, conditions, and events. This consolidated view reduces the need to jump between multiple CLI commands or tools.

The Job page also includes supported lifecycle actions for appropriate states, such as Suspend and Resume, allowing users to act on a Job directly from the UI. Additionally, the plugin provides direct access to Job logs, enabling users to view logs for Pods created by a Volcano Job without leaving the Job detail page. The logs viewer supports both single-Pod and all-Pods views, along with container selection and common log controls such as line count, previous logs, timestamps, and follow.

Queue View: Understanding Resource Allocation

The Queue view provides detailed insights into how resources are being allocated and constrained. It surfaces information such as capacity, allocated resources, deserved and guaranteed resources, reservation details, and child queues. This makes the Queue page particularly useful for understanding how resources are being shared and limited across queues, helping users make more informed decisions about workload prioritization and resource allocation.

PodGroup View: Insights into Gang Scheduling

PodGroups are central to understanding gang scheduling in Volcano, and the plugin makes it easier to inspect their state. The PodGroup view highlights progress, conditions, minimum resource requirements, and more. This provides a clearer picture of whether a workload is blocked due to unmet scheduling conditions, helping users identify and resolve bottlenecks more efficiently.

Map View: Visualizing Resource Relationships

The map view in Headlamp shows how Volcano resources are connected, providing a visual representation of Jobs, PodGroups, Queues, and Pods. This is especially useful when a workload is pending or not progressing as expected. The map can show the Job, its related PodGroup, the Pods created for the workload, and the Queue context around it. Warning and error states also make it easier to spot resources that need attention.

Complementing CLI Tools

The Volcano plugin is not intended to replace CLI tools like kubectl or the Volcano CLI. These tools remain essential for automation, scripting, and raw object inspection. However, the plugin enhances the interactive troubleshooting experience by allowing users to discover related resources more quickly, understand structured detail pages, and move from scheduling state to runtime output without constantly switching tools.

Source: Kubernetes. Analysis based on publicly available information; no hands-on testing is claimed.